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Python Automation for CAE Design Space Exploration

30 Chapters

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CAE design space exploration overview

motivation, challenges, and role of Python automation in parametric studies, DOE, and optimization workflows.

Python environment for CAE automation

package management, virtual environments, Jupyter, and integration with CAE solvers and pre/post-processors.

Parametric geometry generation

scripting CAD kernels and mesh morphing to create design variants for CAE analysis.

Parametricgeometry

Automating CAE solver runs

batch execution, input deck generation, job submission, and monitoring for structural, thermal, and fluid analyses.

Extracting and parsing CAE results

reading output files, databases, and APIs to collect performance metrics and field data.

Design of Experiments (DOE)

sampling methods such as full factorial, Latin hypercube, and Sobol sequences for CAE design space coverage.

Surrogate modeling and response surfaces

building and validating metamodels (Kriging, RBF, polynomial) from CAE simulation data.

Surrogatemodeling

Optimization algorithms in Python

gradient-based, genetic algorithms, and surrogate-assisted optimization for CAE design objectives.

Optimizationalgorithms

Sensitivity analysis and variable screening

identifying influential parameters in CAE models using Python-based methods.

Sensitivityanalysis

Workflow orchestration and parallelization

using Python to manage concurrent CAE jobs, HPC clusters, and cloud resources.

Workfloworchestration

Data management for CAE exploration

structured storage, versioning, and querying of simulation inputs and outputs.

Visualization and dashboards

interactive plotting and reporting of design space results with Python libraries.

Visualizationdashboards

aerodynamic shape optimization of an airfoil using Python-driven CFD and surrogate-based optimization.

aerodynamicoptimization

structural topology and size optimization of a bracket using Python automation and FEA.

structuraltopology

thermal management design exploration for electronics cooling using parametric CAE and Python.

managementexploration

multi-objective crashworthiness design using Python, DOE, and metamodels.

multi-objectivecrashworthiness

battery pack thermal runaway propagation study with automated CAE workflows.

propagationautomated

additive manufacturing process simulation and design space exploration with Python.

additivemanufacturing

vehicle dynamics and suspension design optimization using Python and multibody CAE.

dynamicssuspension

wind turbine blade design exploration coupling Python with FEA and CFD.

explorationcoupling

heat exchanger design optimization using Python-driven CFD and surrogate modeling.

exchangeroptimization

electric motor electromagnetic-thermal design exploration with Python automation.

electricelectromagnetic-thermal

aerospace composite panel design under uncertainty using Python and CAE.

aerospacecomposite

biomedical implant design exploration using Python and FEA for bone remodeling.

biomedicalexploration

marine propeller design optimization with Python and CFD.

propelleroptimization

HVAC duct system design exploration using Python and CFD.

HVACexploration

automotive crash box design for energy absorption using Python and explicit FEA.

automotiveabsorption

gas turbine blade cooling design exploration with Python and conjugate heat transfer.

explorationconjugate

soft robot actuator design exploration using Python and nonlinear FEA.

actuatorexploration

Course summary and future trends

integrating AI/ML, digital twins, and cloud-based CAE design space exploration with Python.

integratingAI/ML